Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)

Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network.

SPONSOR MESSAGES:
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Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto tufalabs.ai
***

TRANSCRIPT + REFS:
dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFIn…

Mohamed Osman (Tufa Labs)
x.com/MohamedOsmanML

Jack Cole (Tufa Labs)
x.com/MindsAI_Jack

How and why deep learning for ARC paper:
github.com/MohamedOsman1998/deep-learning-for-…

TOC:
1. Abstract Reasoning Foundations
[00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview
[00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning
[00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture
[00:20:26] 1.4 Technical Implementation with Long T5 Model

2. ARC Solution Architectures
[00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions
[00:27:54] 2.2 Model Generalization and Function Generation Challenges
[00:32:53] 2.3 Input Representation and VLM Limitations
[00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration
[00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches

3. Advanced Systems Integration
[00:43:00] 3.1 DreamCoder Evolution and LLM Integration
[00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs
[00:54:15] 3.3 ARC v2 Development and Performance Scaling
[00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations
[01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution

REFS:
[00:01:32] Original ARC challenge paper, François Chollet
arxiv.org/abs/1911.01547

[00:06:55] DreamCoder, Kevin Ellis et al.
arxiv.org/abs/2006.08381

[00:12:50] Deep Learning with Python, François Chollet
amazon.com/Deep-Learning-Python-Francois-Choll…

[00:13:35] Deep Learning with Python, François Chollet
amazon.com/Deep-Learning-Python-Francois-Choll…

[00:13:35] Influence of pretraining data for reasoning, Laura Ruis
arxiv.org/abs/2411.12580

[00:17:50] Latent Program Networks, Clement Bonnet
arxiv.org/html/2411.08706v1

[00:20:50] T5, Colin Raffel et al.
arxiv.org/abs/1910.10683

[00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al.
arxiv.org/abs/2411.02272

[00:34:15] Six finger problem, Chen et al.
openaccess.thecvf.com/content/CVPR2024/papers/…

[00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI
huggingface.co/deepseek-ai/DeepSeek-R1-Distill…

[00:40:10] ARC Prize 2024 Technical Report, François Chollet et al.
arxiv.org/html/2412.04604v2

[00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis
arxiv.org/html/2503.15540

[00:54:25] Abstraction and Reasoning Corpus, François Chollet
github.com/fchollet/ARC-AGI

[00:57:10] O3 breakthrough on ARC-AGI, OpenAI
arcprize.org

[00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell
arxiv.org/abs/2305.07141

[01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W.
papers.neurips.cc/paper/9723-mixtape-breaking-…